Simulated oxygen and glucose gradients as a prerequisite for predicting industrial scale performance a priori

Transferring bioprocesses from lab to industrial scale without loss of performance is key for the successful implementation of novel production approaches. Because mixing and mass transfer is usually hampered in large scale, cells experience heterogeneities eventually causing deteriorated yields, that is, reduced titers, productivities, and sugar-to-product conversions. Accordingly, reliable and easy-to-implement tools for a priori prediction of large-scale performance based on dry and wet-lab tests are heavily needed. This study makes use of computational fluid dynamic simulations of a multiphase multi-impeller stirred tank in pilot scale. So-called lifelines, records of 120,000 Corynebacterium glutamicum cells experiencing fluctuating environmental conditions, were identified and used to properly design wet-lab scale-down (SD) devices. Physical parameters such as power input, gas hold up, , and mixing time showed good agreement with experimental measurements. Analyzing the late fed-batch cultivation revealed that the complex double gradient of glucose and oxygen can be translated into a wet-lab SD setup with only few compartments. Most remarkably, the comparison of different mesh sizes outlined that even the coarsest approach with a mesh density of was sufficient to properly predict physical and biological readouts. Accordingly, the approach offers the potential for the thorough analysis of realistic industrial case scenarios.     

Comparing side chain packing in soluble proteins,protein‐protein interfaces,and transmembrane proteins

We compare side chain prediction and packing of core and non‐core regions of soluble proteins, protein‐protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high‐resolution crystal structures of these 3 protein classes. We show that the solvent‐inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein‐protein interfaces and in the membrane‐exposed regions of transmembrane proteins can be predicted by the hard‐sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent‐inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within ) up to a relative solvent accessibility, , for all 3 protein classes. Our results show that % of the interface regions in protein complexes are “core”, that is, densely packed with side chain conformations that can be accurately predicted using the hard‐sphere model. We propose packing fraction as a metric that can be used to distinguish real protein‐protein interactions from designed, non‐binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins.     

Impact of Cavitation,High Shear Stress and Air/Liquid Interfaces on Protein Aggregation

The reported impact of shear stress on protein aggregation has been contradictory. At high shear rates, the occurrence of cavitation or entrapment of air is reasonable and their effects possibly misattributed to shear stress. Nine different proteins (α‐lactalbumin, two antibodies, fibroblast growth factor 2, granulocyte colony stimulating factor [GCSF], green fluorescence protein [GFP], hemoglobin, human serum albumin, and lysozyme) are tested for their aggregation behavior on vapor/liquid interfaces generated by cavitation and compared it to the isolated effects of high shear stress and air/liquid interfaces generated by foaming. Cavitation induced the aggregation of GCSF by +68.9%, hemoglobin +4%, and human serum albumin +2.9%, compared to a control, whereas the other proteins do not aggregate. The protein aggregation behaviors of the different proteins at air/liquid interfaces are similar to cavitation, but the effect is more pronounced. Air‐liquid interface induced the aggregation of GCSF by +94.5%, hemoglobin +35.5%, and human serum albumin (HSA) +31.1%. The results indicate that the sensitivity of a certain protein toward cavitation is very similar to air/liquid‐induced aggregation. Hence, hydroxyl radicals cannot be seen as the driving force for protein aggregation when cavitation occurs. Further, high shear rates of up to 10⁸ s^?1 do not affect any of the tested proteins. Therefore, also within this study generated extremely high isolated shear rates cannot be considered to harm structural integrity when processing proteins.

     

Mean growth rate when rare is not a reliable metric for persistence of species

The coexistence of many species within ecological communities poses a long‐standing theoretical puzzle. Modern coexistence theory (MCT) and related techniques explore this phenomenon by examining the chance of a species population growing from rarity in the presence of all other species. The mean growth rate when rare, , is used in MCT as a metric that measures persistence properties (like invasibility or time to extinction) of a population. Here we critique this reliance on and show that it fails to capture the effect of temporal random abundance variations on persistence properties. The problem becomes particularly severe when an increase in the amplitude of stochastic temporal environmental variations leads to an increase in , since at the same time it enhances random abundance fluctuations and the two effects are inherently intertwined. In this case, the chance of invasion and the mean extinction time of a population may even go down as increases.     

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ON THE COVER: Nereocystis luetkeana, seen here in Port Renfrew, British Columbia, is a canopy-forming kelp that is common in the nearshore waters of the Northeast Pacific. It exhibits pronounced morphological plasticity across hydrodynamic gradients that allows it to minimize drag experienced in flow while maximizing light interception. Photo credit: L. Coleman

     

Estimating the decision curve and its precision from three study designs

The decision curve plots the net benefit of a risk model for making decisions over a range of risk thresholds, corresponding to different ratios of misclassification costs. We discuss three methods to estimate the decision curve, together with corresponding methods of inference and methods to compare two risk models at a given risk threshold. One method uses risks (R) and a binary event indicator (Y) on the entire validation cohort. This method makes no assumptions on how well-calibrated the risk model is nor on the incidence of disease in the population and is comparatively robust to model miscalibration. If one assumes that the model is well-calibrated, one can compute a much more precise estimate of based on risks R alone. However, if the risk model is miscalibrated, serious bias can result. Case–control data can also be used to estimate if the incidence (or prevalence) of the event () is known. This strategy has comparable efficiency to using the full data, and its efficiency is only modestly less than that for the full data if the incidence is estimated from the mean of Y. We estimate variances using influence functions and propose a bootstrap procedure to obtain simultaneous confidence bands around the decision curve for a range of thresholds. The influence function approach to estimate variances can also be applied to cohorts derived from complex survey samples instead of simple random samples.     

Sensitivity analysis and adaptive mutation strategy differential evolution algorithm for optimizing enzymes' turnover numbers in metabolic models

Genome-scale metabolic network model (GSMM) based on enzyme constraints greatly improves general metabolic models. The turnover number ($k_{\mathrm{cat}}$) of enzymes is used as a parameter to limit the reaction when extending GSMM. Therefore, turnover number plays a crucial role in the prediction accuracy of cell metabolism. In this work, we proposed an enzyme-constrained GSMM parameter optimization method. First, sensitivity analysis of the parameters was carried out to select the parameters with the greatest influence on predicting the specific growth rate. Then, differential evolution (DE) algorithm with adaptive mutation strategy was adopted to optimize the parameters. This algorithm can dynamically select five different mutation strategies. Finally, the specific growth rate prediction, flux variability, and phase plane of the optimized model were analyzed to further evaluate the model. The enzyme-constrained GSMM of Saccharomyces cerevisiae, ecYeast8.3.4, was optimized. Results of the sensitivity analysis showed that the optimization variables can be divided into three groups based on sensitivity: most sensitive (149 $k_{\mathrm{cat}}$c), highly sensitive (1759 $k_{\mathrm{cat}}$), and nonsensitive (2502 $k_{\mathrm{cat}}$) groups. Six optimization strategies were developed based on the results of the sensitivity analysis. The results showed that the DE with adaptive mutation strategy can indeed improve the model by optimizing highly sensitive parameters. Retaining all parameters and optimizing the highly sensitive parameters are the recommended optimization strategy.     

New insights into the interaction of proteins and disaccharides—The effect of pH and concentration

To gain new insights into the interaction of proteins and disaccharides, we investigated the hydrodynamic radii, , of lysozyme molecules in solution and in a ternary protein‐sugar‐water system by PFG‐NMR. Our approach is based on the assumption that the anhydrobiotic properties of disaccharides like trehalose are based on aggregation of sugar molecules to the proteins, i.e., accumulation of sugar molecules close to the protein, and that this process can be investigated by the experimentally detectable value of the protein. The R_h values are calculated from the experimentally determined diffusion coefficients and the application of a viscosity correction using the inert molecule dioxane as an internal viscosity reference. The experiments were performed as a function of sugar concentration, the overall particle concentration and the pH value. We investigated the disaccharides trehalose and sucrose, mainly for the reason that trehalose has well know cryptobiotic properties while sucrose, which is similar in size and structure, lacks these properties. The results show the formation of a protective sugar shell around the proteins over a wider range of concentrations and pH values in the case of trehalose.     

Important role of genetic drift in rapid polygenic adaptation

We analyzed a model to determine the factors that facilitate or limit rapid polygenic adaptation. This model includes population genetic terms of mutation and both directional and stabilizing selection on a highly polygenic trait in a diploid population of finite size. First, we derived the equilibrium distribution of the allele frequencies of the multilocus model by diffusion approximation. This formula describing the equilibrium allele frequencies as a mutation‐selection‐drift balance was examined by computer simulation using parameter values inferred for human height, a well‐studied polygenic trait. Second, assuming that a sudden environmental shift of the fitness optimum occurs while the population is in equilibrium, we analyzed the adaptation of the trait to the new optimum. The speed at which the trait mean approaches the new optimum increases with the equilibrium genetic variance. Thus, large population size and/or large mutation rate may facilitate rapid adaptation. Third, the contribution of an individual locus i to polygenic adaptation depends on the compound parameter , where is the effect size, the equilibrium frequency of the trait‐increasing allele of this locus, and . Thus, only loci with large values of this parameter contribute coherently to polygenic adaptation. Given that mutation rates are relatively small, this is more likely in large populations, in which the effects of drift are limited.     

Inverse problem approach to regularized regression models with application to predicting recovery after stroke

Regression modelling is a powerful statistical tool often used in biomedical and clinical research. It could be formulated as an inverse problem that measures the discrepancy between the target outcome and the data produced by representation of the modelled predictors. This approach could simultaneously perform variable selection and coefficient estimation. We focus particularly on a linear regression issue, , where is the parameter of interest and its components are the regression coefficients. The inverse problem finds an estimate for the parameter , which is mapped by the linear operator to the observed outcome data . This problem could be conveyed by finding a solution in the affine subspace . However, in the presence of collinearity, high-dimensional data and high conditioning number of the related covariance matrix, the solution may not be unique, so the introduction of prior information to reduce the subset and regularize the inverse problem is needed. Informed by Huber's robust statistics framework, we propose an optimal regularizer to the regression problem. We compare results of the proposed method and other penalized regression regularization methods: ridge, lasso, adaptive-lasso and elastic-net under different strong hypothesis such as high conditioning number of the covariance matrix and high error amplitude, on both simulated and real data from the South London Stroke Register. The proposed approach can be extended to mixed regression models. Our inverse problem framework coupled with robust statistics methodology offer new insights in statistical regression and learning. It could open a new research development for model fitting and learning.     

The genetic architecture of maternal effects across ontogeny in the red deer

Maternal effects, either environmental or genetic in origin, are an underappreciated source of phenotypic variance in natural populations. Maternal genetic effects have the potential to constrain or enhance the evolution of offspring traits depending on their magnitude and their genetic correlation with direct genetic effects. We estimated the maternal effect variance and its genetic component for 12 traits expressed over the life history in a pedigreed population of wild red deer (morphology, survival/longevity, breeding success). We only found support for maternal genetic effect variance in the two neonatal morphological traits: birth weight ( = 0.31) and birth leg length ( = 0.17). For these two traits, the genetic correlation between maternal and direct additive effects was not significantly different from zero, indicating no constraint to evolution from genetic architecture. In contrast, variance in maternal genetic effects enhanced the additive genetic variance available to respond to natural selection. Maternal effect variance was negligible for late-life traits. We found no evidence for sex differences in either the direct or maternal genetic architecture of offspring traits. Our results suggest that maternal genetic effect variance declines over the lifetime, but also that this additional heritable genetic variation may facilitate evolutionary responses of early-life traits.     

Intuitive and broadly applicable definitions of niche and fitness differences

Explaining nature’s biodiversity is a key challenge for science. To persist, populations must be able to grow faster when rare, a feature called negative frequency dependence and quantified as ‘niche differences’ () in modern coexistence theory. Here, we first show that available definitions of differ in how link to species interactions, are difficult to interpret and often apply to specific community types only. We then present a new definition of that is intuitive and applicable to a broader set of (modelled and empirical) communities than is currently the case, filling a main gap in the literature. Given , we also redefine fitness differences () and illustrate how and determine coexistence. Finally, we demonstrate how to apply our definitions to theoretical models and experimental data, and provide ideas on how they can facilitate comparison and synthesis in community ecology.     

Morphology,Solid Structure and Antioxidant Activity in Vitro of Angelica sinensis Polysaccharide-Ce(IV)

Angelica Sinensis polysaccharide cerium (ASP−Ce) was prepared by Angelica Sinensis polysaccharide (ASP) and cerium ammonium nitrate (NH₄)₂Ce(NO₃)₆. and its morphology, and solid structure was investigated. The antioxidant activity of the ASP−Ce complex in vitro was evaluated. The antioxidant activity of ASP−Ce complex in vitro was evaluated by the scavenging activity of 2,2-diphenyl-1-picrylhyrazyl radical (DPPH), hydroxyl radical (⋅OH) and superoxide anion radical ( ⋅). The results showed that the ASP−Ce had the more ordered structure for inserting the Ce⁴⁺ into the polymer chain of ASP and there was little change in the conformation of the polysaccharide from Ce⁴⁺. Three free radical scavenging experiments proved that ASP−Ce had better antioxidant capacity than of ASP, especially on DPPH, and then on ⋅. The scavenging rate of ASP−Ce at 1.0 mg/mL on DPPH reached 71.6 %. Therefore, these results provide references for the further development and utilization of rare earth-polysaccharide.     

Valid oxygen uptake measurements: using high r2 values with good intentions can bias upward the determination of standard metabolic rate

This analysis shows good intentions in the selection of valid and precise oxygen uptake (O₂) measurements by retaining only slopes of declining dissolved oxygen level in a respirometer that have very high values of the coefficient of determination, r², are not always successful at excluding nonlinear slopes. Much worse, by potentially removing linear slopes that have low r² only because of a low signal-to-noise ratio, this procedure can overestimate the calculation of standard metabolic rate (SMR) of the fish. To remedy this possibility, a few simple diagnostic tools are demonstrated to assess the appropriateness of a given minimum acceptable r², such as calculating the proportion of rejected O₂ determinations, producing a histogram of the r² values and a plot of r² as a function of O₂. The authors offer solutions for cases when many linear slopes have low r². The least satisfactory but easiest to implement is lowering the minimum acceptable r². More satisfactory solutions involve processing (smoothing) the raw signal of dissolved oxygen as a function of time to improve the signal-to-noise ratio and the r²s.     

Optimal Strategies for Managing Wildlife Harvest Under System Change

Wildlife populations are experiencing shifting dynamics due to climate and landscape change. Management policies that fail to account for non-stationary dynamics may fail to achieve management objectives. We establish a framework for understanding optimal strategies for managing a theoretical harvested population under non-stationarity. Building from harvest theory, we develop scenarios representing changes in population growth rate () or carrying capacity () and derive time-dependent optimal harvest policies using stochastic dynamic programming. We then evaluate the cost of falsely assuming stationarity by comparing the outcomes of forward projections in which either the optimal policy or a stationary policy is applied. When declines over time, the stationary policy leads to an underharvest of the population, resulting in less harvest over the short term but leaving the population in a higher-value state. When declines over time, the stationary policy leads to overharvest, resulting in greater harvest returns in the short term but leaving the population in a lower and potentially more vulnerable state. This work demonstrates the basic properties of time-dependent harvest management and provides a framework for evaluating the many outstanding questions about optimal management strategies under climate change. Published 2021. This article is a U.S. Government work and is in the public domain in the USA.     

Estimating the distribution of heterogeneous treatment effects from treatment responses and from a predictive biomarker in a parallel-group RCT: A structural model approach

When the objective is to administer the best of two treatments to an individual, it is necessary to know his or her individual treatment effects (ITEs) and the correlation between the potential responses (PRs) and under treatments 1 and 0. Data that are generated in a parallel-group design RCT does not allow the ITE to be determined because only two samples from the marginal distributions of these PRs are observed and not the corresponding joint distribution. This is due to the “fundamental problem of causal inference.” Here, we present a counterfactual approach for estimating the joint distribution of two normally distributed responses to two treatments. This joint distribution of the PRs and can be estimated by assuming a bivariate normal distribution for the PRs and by using a normally distributed baseline biomarker functionally related to the sum . Such a functional relationship is plausible since a biomarker and the sum encode for the same information in an RCT, namely the variation between subjects. The estimation of the joint trivariate distribution is subjected to some constraints. These constraints can be framed in the context of linear regressions with regard to the proportions of variances in the responses explained and with regard to the residual variation. This presents new insights on the presence of treatment–biomarker interactions. We applied our approach to example data on exercise and heart rate and extended the approach to survival data.     

Isotopically anomalous organic carbon in the aftermath of the Marinoan snowball Earth

Throughout most of the sedimentary record, the marine carbon cycle is interpreted as being in isotopic steady state. This is most commonly inferred via isotopic reconstructions, where two export fluxes (organic carbon and carbonate) are offset by a constant isotopic fractionation of ~25 (termed ). Sedimentary deposits immediately overlying the Marinoan snowball Earth diamictites, however, stray from this prediction. In stratigraphic sections from the Ol Formation (Mongolia) and Sheepbed Formation (Canada), we observe a temporary excursion where the organic matter has anomalously heavy C and is grossly decoupled from the carbonate C. This signal may reflect the unique biogeochemical conditions that persisted in the aftermath of snowball Earth. For example, physical oceanographic modeling suggests that a strong density gradient caused the ocean to remain stratified for about 50,000 years after termination of the Marinoan snowball event, during which time the surface ocean and continental weathering consumed the large atmospheric CO₂ reservoir. Further, we now better understand how C records of carbonate can be post‐depostionally altered and thus be misleading. In an attempt to explain the observed carbon isotope record, we developed a model that tracks the fluxes and isotopic values of carbon between the surface ocean, deep ocean, and atmosphere. By comparing the model output to the sedimentary data, stratification alone cannot generate the anomalous observed isotopic signal. Reproducing the heavy C in organic matter requires the progressively diminishing contribution of an additional anomalous source of organic matter. The exact source of this organic matter is unclear.     

A comprehensive review on bio-mimicked multimolecular frameworks and supramolecules as scaffolds for enzyme immobilization

Immobilization depicts a propitious route to optimize the catalytic performances, efficient recovery, minimizing autocatalysis, and also augment the stabilities of enzymes, particularly in unnatural environments. In this opinion, supramolecules and multimolecular frameworks have captivated immense attention to achieve profound controllable interactions between enzyme molecules and well-defined natural or synthetic architectures to yield protein bioconjugates with high accessibility for substrate binding and enhanced enantioselectivities. This scholastic review emphasizes the possibilities of associating multimolecular complexes with biological entities via several types of interactions, namely covalent interactions, host–guest complexation, $\mathrm{\pi }-\mathrm{\pi }$ interactions, intra/inter hydrogen bondings, electrostatic interactions, and so forth offers remarkable applications for the modulations of enzymes. The potential synergies between artificial supramolecular structures and biological systems are the primary concern of this pedagogical review. The majority of the research primarily focused on the dynamic biomolecule-responsive supramolecular assemblages and multimolecular architectures as ideal platforms for the recognition and modulation of proteins and cells. Embracing sustainable green demeanors of enzyme immobilizations in a quest to reinforce site-selectivity, catalytic efficiency, and structural integrality of enzymes are the contemporary requirements of the biotechnological sectors that instigate the development of novel biocatalytic systems.     

Optimal rotation of insecticides to prevent the evolution of resistance in a structured environment

Several strategies have been used in insecticide resistance management to prevent the evolution of resistance, but the spatial aspects of insecticide application are crucially important among these strategies. Here, we consider a structured environment that consists of on-farm and off-farm fields where crops are planted periodically in on-farm fields during cultivation periods. We define the basic reproduction rate () of resistance as the expected number of offspring of a resistant individual divided by that of a susceptible individual under the condition that the proportion of resistance is extremely small; it is measured as the quantity per cycle of the cultivation period. We calculate using realistic dose-survival curves under a given fitness cost of resistance genes. The evolution of resistance occurs if and only if the value is larger than 0. Then, we propose a procedure for calculating the optimal design of rotational spraying that prevents the evolution of resistance, that is, the evolutionary stable strategy (ESS) for farmers, satisfying the mortality required for managing the abundance of insects. We consider the following controllable factors in calculating the optimal design: the dose of insecticide, the number of sprays, the number of different types of insecticides and potentially, the size of on-farm fields.     

Physical degradation of proteins in well-defined fluid flows studied within a four-roll apparatus

In most applications of biotechnology and downstream processing proteins are exposed to fluid stresses in various flow configurations which often lead to the formation of unwanted protein aggregates. In this paper we present physical degradation experiments for proteins under well-defined flow conditions in a four-roll apparatus. The flow field was characterized numerically by computational fluid dynamics (CFD) and experimentally by particle image velocimetry (PIV). The local shear strain rate as well as the local shear and elongation rate was used to characterize the hydrodynamic stress environment acting on the proteins. Lysozyme was used as a model protein and subjected to well-defined fluid stresses in high and low stress environment. By using in situ turbidity measurements during stressing the aggregate formation was monitored directly in the fluid flow. An increase in absorbance at 350 nm was attributed to a higher content of visible particles (>1 μm). In addition to lysozyme, the formation of aggregates was confirmed for two larger proteins (bovine serum albumin and alcohol dehydrogenase). Thus, the presented experimental setup is a helpful tool to monitor flow-induced protein aggregation with high reproducibility. For instance, screening experiments for formulation development of biopharmaceuticals for fill and finish operations can be performed in the lab-scale in a short time-period if the stress distributions in the application are transferred and applied in the four-roll mill.